ML model deployment - A Data Scientist's nightmare. But why is it such a big pain point? Can we simplify and streamline this process? The answer is YES, you can! With one-click deployment on the AI & Analytics Engine, complex model deployment is a thing of the past.
Only a very small percentage of machine learning projects see the light of the day. Do you know why?
Data scientists fancy playing around with data and then building effective machine learning models in R or a Python Jupyter notebook but that is just half the work. The models are of no use unless they are put in a production environment where they can start making predictions in real-time with live data.
The transition from model development to model deployment is a major challenge for companies, since the skills and experience required in both domains do not overlap and, therefore, requires an efficient workflow to be set up.
Model deployment is the last stage of the machine learning life-cycle and is usually the most complicated. Data scientists feel model deployment falls outside their scope of work and that the task is best left to data engineers or ML engineers as it involves concepts related to DevOps and software development.
Model deployment is a concept that is rarely discussed when you study a course in machine learning. The focus is always on training the algorithm and then fine-tuning it to develop the best possible model. While model development can be done in silos by data scientists, model deployment requires cross-functional team collaboration. The IT team and the business stakeholders need to align on the expectations on how the end-users should be able to interact with the model’s predictions.
Here are a few reasons why that might result in sleepless nights for data scientists.
Generally speaking, data scientists use R or Python to develop their models in an offline environment but the production models usually have to be rewritten in another programming language like Java or C++ to support the existing architecture in place. The code quality also needs to be checked to see if it is possible to deploy the model in a new environment and obtain the same behaviour. When rewriting the model in different languages, the results might show a significant deviation and it might become a challenge to reconcile the results.
Many organizations still rely on legacy systems which makes it extremely difficult to migrate existing software to a new host environment and run it there. To address this, companies first need to get rid of their existing legacy and upgrade to new systems, a transition that can take years.
When the model is being trained in an offline environment, it is usually exposed to a limited amount of data that can fit in the memory of a laptop or PC. However, as the model moves into production, it has to ingest huge volumes of data and it becomes a challenge to scale the application and still meet the same performance criteria. As the user base of the application grows, the model will have to be scaled and made more robust.
You are still not done even after you have deployed your model in a live environment. It needs to be continuously monitored to keep track of issues. Some models need to be retrained to ensure they do not drift away too far from live data. The model might initially perform well in a live environment when it is deployed but if drift issues are not accounted for, model performance will degrade. Hence, even after deployment, the model requires ongoing testing and monitoring to drive optimization.
We have discussed some of the main challenges that can arise in model deployment that can impede an AI project.
Is there a way out? Do things necessarily have to be that complicated for data scientists?
There's a simple solution!
At PI EXCHANGE, we understand the pain and suffering involved in creating business value from AI, be it model development or model deployment. For this very reason, our team has built a one-stop solution for all things data science. The AI & Analytics Engine could serve as your all-weathered friend when it comes to model deployment. It has all the features that you would find irresistible, and we can say with absolute certainty that the Engine will sweep you off your feet. Quite literally!
We strongly recommend you to go through our following blog posts to understand how you can build and deploy machine learning models on the AI & Analytics Engine with a few clicks:
Model deployment is one of the trickiest parts of the machine learning lifecycle. With a scarcity of specialists, most companies find it extremely challenging to have a diverse team in-house that can handle the different facets of model development and deployment.
But does it have to be that hard for businesses to extract value from their data? The answer is NO! The AI & Analytics Engine, for one, could significantly reduce time to market by automating the entire machine learning life cycle. A few clicks will now empower you to get the full value out of your data and stay ahead of the game
Not sure where to start with machine learning? Reach out to us with your business problem, and we’ll get in touch with how the Engine can help you specifically.
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